Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information.

OBJECTIVE

A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently.

METHODS

First, motor unit (MU) firing information was identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison.

RESULTS

Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method.

CONCLUSION

Our approach provides a reliable neural decoding method for dexterous finger movements.

SIGNIFICANCE

Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.